Identification of breast cancer associated variants that modulate transcription factor binding.
Identification of breast cancer associated variants that modulate transcription factor binding.
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DOI：
10.1371/journal.pgen.1006761
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发表时间：
2017-09
期刊：
影响因子：
4.5
通讯作者：
Guertin MJ
中科院分区：
文献类型：
--
作者：
Liu Y;Walavalkar NM;Dozmorov MG;Rich SS;Civelek M;Guertin MJ
全基因组关联研究（GWAS）已经发现了数千个与疾病风险和数量性状相关的基因座，但大多数与风险相关的变异仍然没有特征。大多数GWAS鉴定的基因座富含非编码单核苷酸多态性（SNP），定义风险的分子机制具有挑战性。许多非编码的因果SNP被假设为改变转录因子（TF）结合位点的机制，通过它们影响生物体表型。我们采用整合基因组学方法来鉴定赋予GWAS鉴定的乳腺癌特异性表型的候选TF结合基序。我们进行了从头调控元件的基序分析，分析了已确定的基序的进化保守性，并分析了TF足迹数据，以确定在乳腺癌相关组织和细胞系中招募TF并维持染色质景观的序列元件。我们确定了预测改变乳腺癌相关调控区域内TF结合的候选因果SNP，这些区域与显著相关的GWAS SNP处于强连锁不平衡。我们证实，使用CTCF ChIP-seq数据预测的等位基因特异性偏好的TF结合。我们使用癌症基因组图谱乳腺癌患者数据来鉴定ANKLE 1和ZNF 404作为19p13.11和19q13.31 GWAS鉴定位点中候选TF结合位点SNP的靶基因。这些SNPs与乳腺组织中ZNF 404和ANKLE 1的表达相关。这种综合分析管道是一个通用框架，用于识别调控区和TF结合位点内赋予表型变异和疾病风险的候选因果变异。有效的个性化医疗的前景取决于识别人群中影响疾病风险的遗传变异的能力，然后使用这些信息准确预测个体患者疾病发生的可能性。高风险个体可进入临床试验、临床前干预策略或增加筛查频率以检测早期疾病发作。然而，任何一种遗传变异对增加疾病易感性的贡献通常很小，基因组区域中的许多潜在致病变异与风险相关。因此，重要的是要了解遗传区域内的变异影响疾病易感性的生物学机制，通过将所有变异的集合细化为那些高度可信的因果关系。在此，我们描述了一种方法，将分子基因组学数据与遗传流行病学数据相结合，以了解影响乳腺癌风险的潜在分子机制。这种方法确定了直接调节基因表达以调节疾病易感性的重要转录因子。
Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk. The promise of effective personalized medicine is dependent upon the ability to identify genetic variants in the population that influence disease risk and then use this information to accurately predict the likelihood of disease incidence for individual patients. High-risk individuals may be entered into clinical trails, pre-clinical intervention strategies, or increased frequency of screening to detect early disease onset. However, the contribution of any one genetic variant to increase disease susceptibility is typically small, with many potential causal variants in the genomic region associated with risk. Therefore, it is important to understand the biological mechanisms by which the variants within a genetic region influence disease susceptibility by refining the set of all variants to those that are highly plausible to be causal. Herein, we describe a method to integrate molecular genomics data with genetic epidemiological data to inform on the underlying molecular mechanisms that influence breast cancer risk. This approach identifies the important transcription factors that directly regulate gene expression to modulate disease susceptibility.
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影响因子：
4
作者：
Brachner A;Braun J;Ghodgaonkar M;Castor D;Zlopasa L;Ehrlich V;Jiricny J;Gotzmann J;Knasmüller S;Foisner R
通讯作者：
Foisner R
影响因子：
30.8
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Fuchsberger, Christian
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18.4
作者：
Bhagwat AS;Vakoc CR
通讯作者：
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影响因子：
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通讯作者：
Brown, Myles
影响因子：
10.5
作者：
Bruhn, L;Munnerlyn, A;Grosschedl, R
通讯作者：
Grosschedl, R